Posts Tagged ‘machine learning’

IEOR alum applies machine learning to personalized healthcare

Yonatan Mintz, a 2018 Berkeley IEOR Ph.D. graduate, was recently appointed Assistant Professor at the University of Wisconsin-Madison’s Department of Industrial and Systems Engineering. Mintz applies optimization and machine learning methods to tailor healthcare interventions to individuals. Mintz’s research portfolio includes leveraging patient data to hone personalized health and wellness solutions through wearable technology, to…

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IEOR undergraduates selected as finalists for INFORMS Operations Research Prize

Three former Berkeley IEOR/ORMS undergraduate students, Liangyuan Na (B.A. Operations Research & Management Science ’18), Cong Yang (B.S. Industrial Engineering & Operations Research ’18), and Chi-Cheng Lo (B.S. Industrial Engineering & Operations Research ’18) are finalists in the INFORMS Undergraduate Operations Research Prize Competition. The students were advised by IEOR assistant professor Anil Aswani, and…

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Richard Y. Zhang — Scalable and Guaranteed Computation: Optimization and Machine Learning for the Future Electric Grid

Abstract: Computation promises to greatly enhance the electric grid through optimization and machine learning. However, many computational problems remain unsolved at the scale, speed, and quality necessary for the real world, due to issues of complexity and nonconvexity. In the first part of this talk, we solve the optimization problem known as optimal power flow…

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Cheng-Ju Wu — Machine Learning for Detection and Diagnosis of Disease

Abstract: The presentation will cover our recent developments in machine learning’s application to lung cancer detection and diabetes diagnosis. Lung cancer is the leading cause of cancer deaths world-wide. Early detection of cancer is critical for therapeutic effectiveness and survival improvement. DNA methylation is known to provide potential biomarkers for assessment of cancer risk. Early…

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Grigas to Investigate New Framework For Operations-Driven Machine Learning

IEOR professor Paul Grigas has just been awarded $290,060 by the National Science Foundation to improve operational decision-making by leveraging data and machine learning. Grigas will collaborate with Adam Elamchtoub from Columbia University to advance a new statistical learning framework called Smart “Predict, then Optimize” (SPO) which aims to improve optimization and prediction for better decisions in sectors such as transportation,…

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IEOR Graduate Students Creates New Exercise App Using Machine Learning

IEOR graduate students Mo Zhou and Yonatan Mintz are addressing exercise commitment issues with a new app that uses machine learning to adjust goals on a daily basis. Each New Year around 20% of Americans vow to lose weight, eat healthier, or exercise more. However, for many, this New Year’s resolution is one of the most difficult to keep. Work, school, and daily…

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New York Times showcases AUTOLAB research on robot grasping

Today, the New York Times featured research being conducted at the UC Berkeley Laboratory for Automation Science and Engineering (AUTOLAB) led by IEOR Professor Ken Goldberg. AUTOLAB is breaking new ground in the area of robot grasping by helping robots teach themselves to reliably grasp irregularly-shaped objects that they have never encountered before. The robot uses a neural network to analyze a…

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